R Tutorial
An introduction to R
Introduction
This tutorial is will introduce the reader to , a free, open-source statistical computing environment often used with RStudio, a integrated development environment for .
Download
- Download at https://www.r-project.org/
- Download
RStudioat https://rstudio.com/products/rstudio/download/
Calculator
can be used as a super awesome calculator
## [1] 8
## [1] 8
## [1] 8
## [1] 8
## [1] 8
Functions
has many useful built in functions
## [1] 1 2 3 4 5 6 7 8 9 10
## [1] "1" "2" "3" "4" "5" "6" "7" "8" "9" "10"
## [1] 1 2 1 2 1 2 1 2 1 2
## [1] 1 2 3 4 5 1 2 3 4 5
## [1] 1 1 2 2 3 3 4 4 5 5
## [1] 1 2 3 4 5 1 2
## [1] 5 10 15 20 25 30 35 40 45 50
## [1] 5.00 16.25 27.50 38.75 50.00
## [1] "1-20" "2-21" "3-22" "4-23" "5-24" "6-25" "7-26" "8-27" "9-28" "10-29" "1-30"
## [1] "1-2-3-4-5-6-7-8-9-10"
## [1] "x1" "x2" "x3" "x4" "x5" "x6" "x7" "x8" "x9" "x10"
## [1] 1
## [1] 10
## [1] 1 10
## [1] 5.5
## [1] 3.02765
Custom Functions
Users can also create their own functions
customFunction1 <- function(x, y) {
z <- 100 * x / (x + y)
paste(z, "%")
}
customFunction1(x = 10, y = 90)## [1] "10 %"
customFunction2 <- function(x) {
mymin <- mean(x - sd(x))
mymax <- mean(x) + sd(x)
print(paste("Min =", mymin))
print(paste("Max =", mymax))
}
customFunction2(x = 1:10)## [1] "Min = 2.47234964590251"
## [1] "Max = 8.52765035409749"
for loops and if else
statements
## [1] 3 6 9 12 15 18 21 24 27 30
## [1] 1 0 1 0 1 0 1 0 1 0
for(i in 1:length(xx)) {
if((xx[i] %% 2) == 0) {
print(paste(xx[i],"is Even"))
} else {
print(paste(xx[i],"is Odd"))
}
}## [1] "3 is Odd"
## [1] "6 is Even"
## [1] "9 is Odd"
## [1] "12 is Even"
## [1] "15 is Odd"
## [1] "18 is Even"
## [1] "21 is Odd"
## [1] "24 is Even"
## [1] "27 is Odd"
## [1] "30 is Even"
## [1] "Odd" "Even" "Odd" "Even" "Odd" "Even" "Odd" "Even" "Odd" "Even"
## [1] "3 is Odd" "6 is Even" "9 is Odd" "12 is Even" "15 is Odd" "18 is Even" "21 is Odd" "24 is Even" "27 is Odd" "30 is Even"
Objects
Information can be stored in user defined objects, in multiple forms:
c(): a string of valuesmatrix(): a two dimensional matrix in one formatdata.frame(): a two dimensional matrix where each column can be a different formatlist():
A string…
## [1] 1 2 3 4 5 6 7 8 9 10
## [1] 1 2 3 4 5 6 7 8 9 10
A matrix…
## [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10]
## [1,] 1 2 3 4 5 6 7 8 9 10
## [2,] 11 12 13 14 15 16 17 18 19 20
## [3,] 21 22 23 24 25 26 27 28 29 30
## [4,] 31 32 33 34 35 36 37 38 39 40
## [5,] 41 42 43 44 45 46 47 48 49 50
## [6,] 51 52 53 54 55 56 57 58 59 60
## [7,] 61 62 63 64 65 66 67 68 69 70
## [8,] 71 72 73 74 75 76 77 78 79 80
## [9,] 81 82 83 84 85 86 87 88 89 90
## [10,] 91 92 93 94 95 96 97 98 99 100
## [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10]
## [1,] 1 11 21 31 41 51 61 71 81 91
## [2,] 2 12 22 32 42 52 62 72 82 92
## [3,] 3 13 23 33 43 53 63 73 83 93
## [4,] 4 14 24 34 44 54 64 74 84 94
## [5,] 5 15 25 35 45 55 65 75 85 95
## [6,] 6 16 26 36 46 56 66 76 86 96
## [7,] 7 17 27 37 47 57 67 77 87 97
## [8,] 8 18 28 38 48 58 68 78 88 98
## [9,] 9 19 29 39 49 59 69 79 89 99
## [10,] 10 20 30 40 50 60 70 80 90 100
A data frame…
xd <- data.frame(
x1 = c("aa","bb","cc","dd","ee",
"ff","gg","hh","ii","jj"),
x2 = 1:10,
x3 = c(1,1,1,1,1,2,2,2,3,3),
x4 = rep(c(1,2), times = 5),
x5 = rep(1:5, times = 2),
x6 = rep(1:5, each = 2),
x7 = seq(5, 50, by = 5),
x8 = log10(1:10),
x9 = (1:10)^3,
x10 = c(T,T,T,F,F,T,T,F,F,F)
)
xd## x1 x2 x3 x4 x5 x6 x7 x8 x9 x10
## 1 aa 1 1 1 1 1 5 0.0000000 1 TRUE
## 2 bb 2 1 2 2 1 10 0.3010300 8 TRUE
## 3 cc 3 1 1 3 2 15 0.4771213 27 TRUE
## 4 dd 4 1 2 4 2 20 0.6020600 64 FALSE
## 5 ee 5 1 1 5 3 25 0.6989700 125 FALSE
## 6 ff 6 2 2 1 3 30 0.7781513 216 TRUE
## 7 gg 7 2 1 2 4 35 0.8450980 343 TRUE
## 8 hh 8 2 2 3 4 40 0.9030900 512 FALSE
## 9 ii 9 3 1 4 5 45 0.9542425 729 FALSE
## 10 jj 10 3 2 5 5 50 1.0000000 1000 FALSE
A list…
## [1] 1 2 3 4 5 6 7 8 9 10
## [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10]
## [1,] 1 11 21 31 41 51 61 71 81 91
## [2,] 2 12 22 32 42 52 62 72 82 92
## [3,] 3 13 23 33 43 53 63 73 83 93
## [4,] 4 14 24 34 44 54 64 74 84 94
## [5,] 5 15 25 35 45 55 65 75 85 95
## [6,] 6 16 26 36 46 56 66 76 86 96
## [7,] 7 17 27 37 47 57 67 77 87 97
## [8,] 8 18 28 38 48 58 68 78 88 98
## [9,] 9 19 29 39 49 59 69 79 89 99
## [10,] 10 20 30 40 50 60 70 80 90 100
## x1 x2 x3 x4 x5 x6 x7 x8 x9 x10
## 1 aa 1 1 1 1 1 5 0.0000000 1 TRUE
## 2 bb 2 1 2 2 1 10 0.3010300 8 TRUE
## 3 cc 3 1 1 3 2 15 0.4771213 27 TRUE
## 4 dd 4 1 2 4 2 20 0.6020600 64 FALSE
## 5 ee 5 1 1 5 3 25 0.6989700 125 FALSE
## 6 ff 6 2 2 1 3 30 0.7781513 216 TRUE
## 7 gg 7 2 1 2 4 35 0.8450980 343 TRUE
## 8 hh 8 2 2 3 4 40 0.9030900 512 FALSE
## 9 ii 9 3 1 4 5 45 0.9542425 729 FALSE
## 10 jj 10 3 2 5 5 50 1.0000000 1000 FALSE
Selecting Data
## [1] 5
## [1] 1
## [1] 1
## [1] 1 1 1 1 1 2 2 2 3 3
## [1] 1 1 1 1 1 2 2 2 3 3
## x1 x2 x3 x4 x5 x6 x7 x8 x9 x10
## 3 cc 3 1 1 3 2 15 0.4771213 27 TRUE
## x4 x5
## 2 2 2
## 4 2 4
## [1] "aa" "bb" "cc" "dd" "ee" "ff" "gg" "hh" "ii" "jj"
regexpr
xx <- data.frame(Name = c("Item 1 (detail 1)",
"Item 20 (detail 20)",
"Item 300 (detail 300)"),
Item = NA,
Detail = NA)
xx$Detail <- substr(xx$Name, regexpr("\\(", xx$Name)+1, regexpr("\\)", xx$Name)-1)
xx$Item <- substr(xx$Name, 1, regexpr("\\(", xx$Name)-2)
xx## Name Item Detail
## 1 Item 1 (detail 1) Item 1 detail 1
## 2 Item 20 (detail 20) Item 20 detail 20
## 3 Item 300 (detail 300) Item 300 detail 300
Data Formats
Data can also be saved in many formats:
- numeric
- integer
- character
- factor
- logical
## [1] "1" "1" "1" "1" "1" "2" "2" "2" "3" "3"
## [1] 1 1 1 1 1 2 2 2 3 3
## [1] 1 1 1 1 1 2 2 2 3 3
## Levels: 1 2 3
## [1] 1 1 1 1 1 2 2 2 3 3
## Levels: 3 2 1
## [1] TRUE TRUE TRUE FALSE FALSE TRUE TRUE FALSE FALSE FALSE
## [1] 1 1 1 0 0 1 1 0 0 0
## [1] 5
Internal structure of an object can be checked with
str()
## num [1:10] 1 2 3 4 5 6 7 8 9 10
## int [1:10, 1:10] 1 2 3 4 5 6 7 8 9 10 ...
## 'data.frame': 10 obs. of 10 variables:
## $ x1 : chr "aa" "bb" "cc" "dd" ...
## $ x2 : int 1 2 3 4 5 6 7 8 9 10
## $ x3 : Factor w/ 3 levels "3","2","1": 3 3 3 3 3 2 2 2 1 1
## $ x4 : num 1 2 1 2 1 2 1 2 1 2
## $ x5 : int 1 2 3 4 5 1 2 3 4 5
## $ x6 : int 1 1 2 2 3 3 4 4 5 5
## $ x7 : num 5 10 15 20 25 30 35 40 45 50
## $ x8 : num 0 0.301 0.477 0.602 0.699 ...
## $ x9 : num 1 8 27 64 125 216 343 512 729 1000
## $ x10: logi TRUE TRUE TRUE FALSE FALSE TRUE ...
## List of 3
## $ : num [1:10] 1 2 3 4 5 6 7 8 9 10
## $ : int [1:10, 1:10] 1 2 3 4 5 6 7 8 9 10 ...
## $ :'data.frame': 10 obs. of 10 variables:
## ..$ x1 : chr [1:10] "aa" "bb" "cc" "dd" ...
## ..$ x2 : int [1:10] 1 2 3 4 5 6 7 8 9 10
## ..$ x3 : num [1:10] 1 1 1 1 1 2 2 2 3 3
## ..$ x4 : num [1:10] 1 2 1 2 1 2 1 2 1 2
## ..$ x5 : int [1:10] 1 2 3 4 5 1 2 3 4 5
## ..$ x6 : int [1:10] 1 1 2 2 3 3 4 4 5 5
## ..$ x7 : num [1:10] 5 10 15 20 25 30 35 40 45 50
## ..$ x8 : num [1:10] 0 0.301 0.477 0.602 0.699 ...
## ..$ x9 : num [1:10] 1 8 27 64 125 216 343 512 729 1000
## ..$ x10: logi [1:10] TRUE TRUE TRUE FALSE FALSE TRUE ...
Packages
Additional libraries can be installed and loaded for use.
library(scales)
xx <- data.frame(Values = 1:10)
xx$Rescaled <- rescale(x = xx$Values, to = c(1,30))
xx## Values Rescaled
## 1 1 1.000000
## 2 2 4.222222
## 3 3 7.444444
## 4 4 10.666667
## 5 5 13.888889
## 6 6 17.111111
## 7 7 20.333333
## 8 8 23.555556
## 9 9 26.777778
## 10 10 30.000000
libraries can also be used without having to load them
## [1] 1.000000 4.222222 7.444444 10.666667 13.888889 17.111111 20.333333 23.555556 26.777778 30.000000
Data Wrangling
R for Data Science - https://r4ds.had.co.nz/
xx <- data.frame(Group = c("X","X","Y","Y","Y","X","X","X","Y","Y"),
Data1 = 1:10,
Data2 = seq(10, 100, by = 10))
xx$NewData1 <- xx$Data1 + xx$Data2
xx$NewData2 <- xx$Data1 * 1000
xx## Group Data1 Data2 NewData1 NewData2
## 1 X 1 10 11 1000
## 2 X 2 20 22 2000
## 3 Y 3 30 33 3000
## 4 Y 4 40 44 4000
## 5 Y 5 50 55 5000
## 6 X 6 60 66 6000
## 7 X 7 70 77 7000
## 8 X 8 80 88 8000
## 9 Y 9 90 99 9000
## 10 Y 10 100 110 10000
## [1] TRUE TRUE TRUE TRUE FALSE FALSE FALSE FALSE FALSE FALSE
## Group Data1 Data2 NewData1 NewData2
## 1 X 1 10 11 1000
## 2 X 2 20 22 2000
## 3 Y 3 30 33 3000
## 4 Y 4 40 44 4000
## Group Data2 NewData1
## 1 X 10 11
## 2 X 20 22
## 6 X 60 66
## 7 X 70 77
## 8 X 80 88
Data wrangling with tidyverse and pipes
(%>%)
library(tidyverse) # install.packages("tidyverse")
xx <- data.frame(Group = c("X","X","Y","Y","Y","Y","Y","X","X","X")) %>%
mutate(Data1 = 1:10,
Data2 = seq(10, 100, by = 10),
NewData1 = Data1 + Data2,
NewData2 = Data1 * 1000)
xx## Group Data1 Data2 NewData1 NewData2
## 1 X 1 10 11 1000
## 2 X 2 20 22 2000
## 3 Y 3 30 33 3000
## 4 Y 4 40 44 4000
## 5 Y 5 50 55 5000
## 6 Y 6 60 66 6000
## 7 Y 7 70 77 7000
## 8 X 8 80 88 8000
## 9 X 9 90 99 9000
## 10 X 10 100 110 10000
## Group Data1 Data2 NewData1 NewData2
## 1 X 1 10 11 1000
## 2 X 2 20 22 2000
## 3 Y 3 30 33 3000
## 4 Y 4 40 44 4000
## Group Data1 Data2 NewData1 NewData2
## 1 X 1 10 11 1000
## 2 X 2 20 22 2000
## 3 Y 3 30 33 3000
## 4 Y 4 40 44 4000
## Group NewColName NewData1
## 1 X 10 11
## 2 X 20 22
## 3 X 80 88
## 4 X 90 99
## 5 X 100 110
xs <- xx %>%
group_by(Group) %>%
summarise(Data2_mean = mean(Data2),
Data2_sd = sd(Data2),
NewData2_mean = mean(NewData2),
NewData2_sd = sd(NewData2))
xs## # A tibble: 2 × 5
## Group Data2_mean Data2_sd NewData2_mean NewData2_sd
## <chr> <dbl> <dbl> <dbl> <dbl>
## 1 X 60 41.8 6000 4183.
## 2 Y 50 15.8 5000 1581.
## Group Data1 Data2 NewData1 NewData2 Data2_mean Data2_sd NewData2_mean NewData2_sd
## 1 X 1 10 11 1000 60 41.83300 6000 4183.300
## 2 X 2 20 22 2000 60 41.83300 6000 4183.300
## 3 Y 3 30 33 3000 50 15.81139 5000 1581.139
## 4 Y 4 40 44 4000 50 15.81139 5000 1581.139
## 5 Y 5 50 55 5000 50 15.81139 5000 1581.139
## 6 Y 6 60 66 6000 50 15.81139 5000 1581.139
## 7 Y 7 70 77 7000 50 15.81139 5000 1581.139
## 8 X 8 80 88 8000 60 41.83300 6000 4183.300
## 9 X 9 90 99 9000 60 41.83300 6000 4183.300
## 10 X 10 100 110 10000 60 41.83300 6000 4183.300
Read/Write data
For excel sheets, the package readxl can be used to read
in sheets of data.
library(readxl) # install.packages("readxl")
xx <- read_xlsx("data_r_tutorial.xlsx", sheet = "Data")Tidy Data
- Tutorial 1 - https://cran.r-project.org/web/packages/tidyr/vignettes/tidy-data.html
- Tutorial 2 - https://r4ds.had.co.nz/tidy-data.html
yy <- xx %>%
group_by(Name, Location) %>%
summarise(Mean_DTF = round(mean(DTF),1)) %>%
arrange(Location)
yy## # A tibble: 9 × 3
## # Groups: Name [3]
## Name Location Mean_DTF
## <chr> <chr> <dbl>
## 1 CDC Maxim AGL Jessore, Bangladesh 86.7
## 2 ILL 618 AGL Jessore, Bangladesh 79.3
## 3 Laird AGL Jessore, Bangladesh 76.8
## 4 CDC Maxim AGL Metaponto, Italy 134.
## 5 ILL 618 AGL Metaponto, Italy 138.
## 6 Laird AGL Metaponto, Italy 137.
## 7 CDC Maxim AGL Saskatoon, Canada 52.5
## 8 ILL 618 AGL Saskatoon, Canada 47
## 9 Laird AGL Saskatoon, Canada 56.8
## # A tibble: 3 × 4
## # Groups: Name [3]
## Name `Jessore, Bangladesh` `Metaponto, Italy` `Saskatoon, Canada`
## <chr> <dbl> <dbl> <dbl>
## 1 CDC Maxim AGL 86.7 134. 52.5
## 2 ILL 618 AGL 79.3 138. 47
## 3 Laird AGL 76.8 137. 56.8
## # A tibble: 9 × 3
## # Groups: Name [3]
## Name TraitName Value
## <chr> <chr> <dbl>
## 1 CDC Maxim AGL Jessore, Bangladesh 86.7
## 2 ILL 618 AGL Jessore, Bangladesh 79.3
## 3 Laird AGL Jessore, Bangladesh 76.8
## 4 CDC Maxim AGL Metaponto, Italy 134.
## 5 ILL 618 AGL Metaponto, Italy 138.
## 6 Laird AGL Metaponto, Italy 137.
## 7 CDC Maxim AGL Saskatoon, Canada 52.5
## 8 ILL 618 AGL Saskatoon, Canada 47
## 9 Laird AGL Saskatoon, Canada 56.8
## # A tibble: 3 × 4
## TraitName `CDC Maxim AGL` `ILL 618 AGL` `Laird AGL`
## <chr> <dbl> <dbl> <dbl>
## 1 Jessore, Bangladesh 86.7 79.3 76.8
## 2 Metaponto, Italy 134. 138. 137.
## 3 Saskatoon, Canada 52.5 47 56.8
Base Plotting
We will start with some basic plotting using the base function
plot()
Now lets create some random and normally distributed data to make some more complicated plots
# 100 random uniformly distributed numbers ranging from 0 - 100
ru <- runif(100, min = 0, max = 100)
ru## [1] 3.1064849 80.4942227 9.3177476 41.6953321 52.6163028 15.0653420 23.0527087 75.7843528 7.3517574 65.4448673 12.8992963 91.3463365 31.8887076
## [14] 98.5832302 24.6541394 84.1345802 53.1813784 65.1984460 87.2945299 74.8378533 38.1153114 72.2003065 17.2648088 59.6784910 84.4266001 55.7015626
## [27] 62.3212670 73.9350175 44.2927258 34.9344449 19.6741890 71.8432390 61.3397032 6.9095320 6.5739968 33.5660168 50.6452079 45.2686358 1.4576352
## [40] 0.8875183 86.6365662 47.6194602 56.0799747 13.2306780 59.4752554 94.6262646 44.2608368 86.3901390 4.7076691 35.9149042 26.2439965 80.0190398
## [53] 90.4512395 19.2019676 91.9632807 87.7009476 50.8332442 50.2392547 27.7566784 72.1311537 63.5051010 59.2673433 68.4311401 96.4012583 49.8999687
## [66] 8.2321092 52.6342215 79.6263677 34.6291410 87.1007387 31.0044552 14.0764991 19.2965159 42.9394660 49.3305554 43.7396558 82.4319560 6.6637634
## [79] 44.6270275 25.8243337 95.1429195 3.1076425 79.5916120 78.2208920 75.9373365 86.5610414 56.7814301 26.3316108 17.3998654 99.9060261 48.0474111
## [92] 98.7457506 27.8377269 48.6823851 40.1273385 82.5350349 96.2563243 65.1377174 79.0038614 74.2689355
## [1] 40 39 1 82 49 35 78 34 9 66 3 11 44 72 6 23 89 54 73 31 7 15 80 51 88 59 93 71 13 36 69 30 50 21 95 4
## [37] 74 76 47 29 79 38 42 91 94 75 65 58 37 57 5 67 17 26 43 87 62 45 24 33 27 61 98 18 10 63 32 60 22 28 100 20
## [73] 8 85 84 99 83 68 52 2 77 96 16 25 48 86 41 70 19 56 53 12 55 46 81 97 64 14 92 90
## [1] 0.8875183 1.4576352 3.1064849 3.1076425 4.7076691 6.5739968 6.6637634 6.9095320 7.3517574 8.2321092 9.3177476 12.8992963 13.2306780
## [14] 14.0764991 15.0653420 17.2648088 17.3998654 19.2019676 19.2965159 19.6741890 23.0527087 24.6541394 25.8243337 26.2439965 26.3316108 27.7566784
## [27] 27.8377269 31.0044552 31.8887076 33.5660168 34.6291410 34.9344449 35.9149042 38.1153114 40.1273385 41.6953321 42.9394660 43.7396558 44.2608368
## [40] 44.2927258 44.6270275 45.2686358 47.6194602 48.0474111 48.6823851 49.3305554 49.8999687 50.2392547 50.6452079 50.8332442 52.6163028 52.6342215
## [53] 53.1813784 55.7015626 56.0799747 56.7814301 59.2673433 59.4752554 59.6784910 61.3397032 62.3212670 63.5051010 65.1377174 65.1984460 65.4448673
## [66] 68.4311401 71.8432390 72.1311537 72.2003065 73.9350175 74.2689355 74.8378533 75.7843528 75.9373365 78.2208920 79.0038614 79.5916120 79.6263677
## [79] 80.0190398 80.4942227 82.4319560 82.5350349 84.1345802 84.4266001 86.3901390 86.5610414 86.6365662 87.1007387 87.2945299 87.7009476 90.4512395
## [92] 91.3463365 91.9632807 94.6262646 95.1429195 96.2563243 96.4012583 98.5832302 98.7457506 99.9060261
# 100 normally distributed numbers with a mean of 50 and sd of 10
nd <- rnorm(100, mean = 50, sd = 10)
nd## [1] 55.25233 42.12342 36.22120 62.34149 46.59710 37.38636 51.34111 50.98472 24.07697 37.52985 43.37467 58.77059 58.01773 60.60991 61.53663 40.50322
## [17] 59.12070 39.41423 44.38440 54.43414 54.56032 41.36890 42.66124 69.95507 48.38819 58.95973 46.04603 63.03538 60.57480 65.11524 42.86947 45.95879
## [33] 67.23158 48.04938 52.09641 58.34126 43.74923 44.70364 61.98227 40.65705 62.14422 43.81829 48.31415 61.01606 60.20599 50.17016 50.58423 45.60665
## [49] 70.23086 37.66952 58.99186 48.29839 48.50482 53.96008 30.28507 53.87556 66.41095 51.71167 62.47530 49.86052 47.56915 56.90077 69.76683 54.59128
## [65] 61.60579 48.97409 50.64567 41.34101 58.65059 68.62365 48.95999 50.43981 37.18072 45.93589 58.21670 40.82761 72.85805 45.74952 42.31256 45.00740
## [81] 66.28392 37.00301 54.51852 67.06012 45.35291 67.74008 40.00254 72.05380 43.26492 41.14627 58.75652 29.50265 50.85689 64.34900 54.17219 51.89696
## [97] 62.95465 56.22857 69.10242 66.94796
## [1] 24.07697 29.50265 30.28507 36.22120 37.00301 37.18072 37.38636 37.52985 37.66952 39.41423 40.00254 40.50322 40.65705 40.82761 41.14627 41.34101
## [17] 41.36890 42.12342 42.31256 42.66124 42.86947 43.26492 43.37467 43.74923 43.81829 44.38440 44.70364 45.00740 45.35291 45.60665 45.74952 45.93589
## [33] 45.95879 46.04603 46.59710 47.56915 48.04938 48.29839 48.31415 48.38819 48.50482 48.95999 48.97409 49.86052 50.17016 50.43981 50.58423 50.64567
## [49] 50.85689 50.98472 51.34111 51.71167 51.89696 52.09641 53.87556 53.96008 54.17219 54.43414 54.51852 54.56032 54.59128 55.25233 56.22857 56.90077
## [65] 58.01773 58.21670 58.34126 58.65059 58.75652 58.77059 58.95973 58.99186 59.12070 60.20599 60.57480 60.60991 61.01606 61.53663 61.60579 61.98227
## [81] 62.14422 62.34149 62.47530 62.95465 63.03538 64.34900 65.11524 66.28392 66.41095 66.94796 67.06012 67.23158 67.74008 68.62365 69.10242 69.76683
## [97] 69.95507 70.23086 72.05380 72.85805
ggplot2
Lets be honest, the base plots are ugly! The ggplot2
package gives the user to create a better, more visually appealing
plots. Additional packages such as ggbeeswarm and
ggrepel also contain useful functions to add to the
functionality of ggplot2.
- ggplot2 - https://ggplot2.tidyverse.org/
- Tutorial 1 - http://r-statistics.co/ggplot2-Tutorial-With-R.html
- Tutorial 2 - https://www.statsandr.com/blog/graphics-in-r-with-ggplot2/
- The R Graph Gallery - https://www.r-graph-gallery.com/ggplot2-package.html
xx <- data.frame(data = c(rnorm(50, mean = 40, sd = 10),
rnorm(50, mean = 60, sd = 5)),
group = factor(rep(1:2, each = 50)),
label = c("Label1", rep(NA, 49), "Label2", rep(NA, 49)))
mp <- ggplot(xx, aes(x = data, fill = group))
mp + geom_histogram(color = "black")mp2 <- mp + geom_violin() +
geom_boxplot(width = 0.1, fill = "white") +
geom_beeswarm(alpha = 0.5)
library(ggrepel)
mp2 + geom_text_repel(aes(label = label), nudge_x = 0.4)library(ggpubr)
ggarrange(mp1, mp2, ncol = 2, widths = c(2,1),
common.legend = T, legend = "bottom")Statistics
- Handbook of Biological Statistics - http://biostathandbook.com/
- R Companion for ^ - https://rcompanion.org/rcompanion/a_02.html
# Prep data
lev_Loc <- c("Saskatoon, Canada", "Jessore, Bangladesh", "Metaponto, Italy")
lev_Name <- c("ILL 618 AGL", "CDC Maxim AGL", "Laird AGL")
dd <- read_xlsx("data_r_tutorial.xlsx", sheet = "Data") %>%
mutate(Location = factor(Location, levels = lev_Loc),
Name = factor(Name, levels = lev_Name))
xx <- dd %>%
group_by(Name, Location) %>%
summarise(Mean_DTF = mean(DTF))
xx %>% spread(Location, Mean_DTF)## # A tibble: 3 × 4
## # Groups: Name [3]
## Name `Saskatoon, Canada` `Jessore, Bangladesh` `Metaponto, Italy`
## <fct> <dbl> <dbl> <dbl>
## 1 ILL 618 AGL 47 79.3 138.
## 2 CDC Maxim AGL 52.5 86.7 134.
## 3 Laird AGL 56.8 76.8 137.
# Plot
mp1 <- ggplot(dd, aes(x = Location, y = DTF, color = Name, shape = Name)) +
geom_point(size = 2, alpha = 0.7, position = position_dodge(width=0.5))
mp2 <- ggplot(xx, aes(x = Location, y = Mean_DTF,
color = Name, group = Name, shape = Name)) +
geom_point(size = 2.5, alpha = 0.7) +
geom_line(size = 1, alpha = 0.7) +
theme(legend.position = "top")
ggarrange(mp1, mp2, ncol = 2, common.legend = T, legend = "top")From first glace, it is clear there are differences between genotypes, locations, and genotype x environment (GxE) interactions. Now let’s do a few statistical tests.
## Df Sum Sq Mean Sq F value Pr(>F)
## Name 2 88 44 3.476 0.0395 *
## Location 2 65863 32932 2598.336 < 2e-16 ***
## Name:Location 4 560 140 11.044 2.52e-06 ***
## Residuals 45 570 13
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
As expected, an ANOVA shows statistical significance for genotype (p-value = 0.0395), Location (p-value < 2e-16) and GxE interactions (p-value < 2.52e-06). However, all this tells us is that one genotype is different from the rest, one location is different from the others and that there is GxE interactions. If we want to be more specific, would need to do some multiple comparison tests.
If we only have two things to compare, we could do a t-test.
xx <- dd %>%
filter(Location %in% c("Saskatoon, Canada", "Jessore, Bangladesh")) %>%
spread(Location, DTF)
t.test(x = xx$`Saskatoon, Canada`, y = xx$`Jessore, Bangladesh`)##
## Welch Two Sample t-test
##
## data: xx$`Saskatoon, Canada` and xx$`Jessore, Bangladesh`
## t = -17.521, df = 32.701, p-value < 2.2e-16
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -32.18265 -25.48402
## sample estimates:
## mean of x mean of y
## 52.11111 80.94444
DTF in Saskatoon, Canada is significantly different (p-value < 2.2e-16) from DTF in Jessore, Bangladesh.
xx <- dd %>%
filter(Name %in% c("ILL 618 AGL", "Laird AGL"),
Location == "Metaponto, Italy") %>%
spread(Name, DTF)
t.test(x = xx$`ILL 618 AGL`, y = xx$`Laird AGL`)##
## Welch Two Sample t-test
##
## data: xx$`ILL 618 AGL` and xx$`Laird AGL`
## t = 0.38008, df = 8.0564, p-value = 0.7137
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -5.059739 7.059739
## sample estimates:
## mean of x mean of y
## 137.8333 136.8333
DTF between ILL 618 AGL and Laird AGL are not significantly different (p-value = 0.7137) in Metaponto, Italy.
pch Plot
xx <- data.frame(x = rep(1:6, times = 5, length.out = 26),
y = rep(5:1, each = 6, length.out = 26),
pch = 0:25)
mp <- ggplot(xx, aes(x = x, y = y, shape = as.factor(pch))) +
geom_point(color = "darkred", fill = "darkblue", size = 5) +
geom_text(aes(label = pch), nudge_x = -0.25) +
scale_shape_manual(values = xx$pch) +
scale_x_continuous(breaks = 6:1) +
scale_y_continuous(breaks = 6:1) +
theme_void() +
theme(legend.position = "none",
plot.title = element_text(hjust = 0.5),
plot.subtitle = element_text(hjust = 0.5),
axis.text = element_blank(),
axis.ticks = element_blank()) +
labs(title = "Plot symbols in R (pch)",
subtitle = "color = \"darkred\", fill = \"darkblue\"",
x = NULL, y = NULL)
ggsave("pch.png", mp, width = 4.5, height = 3, bg = "white")R Markdown
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